A Conceptual model for cognitive management of manufacturing systems to increase competitiveness

Abstract

The competitiveness of manufacturing systems is increasingly influenced by their ability to dynamically adapt to demand variations, economic constraints, and operational conditions. Classical manufacturing management approaches, based on static strategies or local optimizations, are often insufficient to meet these requirements in a dynamic industrial environment. In this context, the paper proposes a conceptual framework for cognitive management of manufacturing systems, based on a cyclic cognitive loop, which integrates continuous perception of the system state, performance analysis, decision mechanisms, and adaptive feedback. The cognitive loop is formulated by correlating the principles of cognitive engineering with machine learning techniques, with the explicit objective of maintaining and improving the competitiveness of the manufacturing system. The novelty of the paper consists in the structuring of a cognitive loop dedicated to competitive management, in which decisions to adapt manufacturing processes are guided by a quantifiable economic indicator, expressed by the unit cost of production. Unlike existing approaches oriented mainly towards digitalization and connectivity, the proposed framework emphasizes the cognitive autonomy of the manufacturing system and the direct link between cognitive mechanisms and economic performance.

The paper has a conceptual and methodological character, providing a theoretical basis for further developments regarding mathematical modeling, experimental validation, and industrial implementation of the proposed cognitive loop.